public void SaveNeuralNet(DrinksSelfOrganizingMap drinksSelfOrganizingMap, string filename)
{
using (XmlWriter writer = XmlWriter.Create(filename))
{
writer.WriteStartDocument();
writer.WriteStartElement("NeuralNet");
writer.WriteStartElement("Meta");
writer.WriteElementString("Width", drinksSelfOrganizingMap.NeuralMapWidth.ToString());
writer.WriteElementString("Height", drinksSelfOrganizingMap.NeuralMapHeight.ToString());
writer.WriteElementString("DistanceBetweenNeurons", drinksSelfOrganizingMap.DistanceBetweenNeurons.ToString());
writer.WriteElementString("VectorQuantizationError", drinksSelfOrganizingMap.VectorQuantizationError.ToString());
writer.WriteElementString("VectorQuantizationErrorStandardDeviation", drinksSelfOrganizingMap.VectorQuantizationErrorStandardDeviation.ToString());
writer.WriteEndElement();
writer.WriteStartElement("Data");
StringBuilder sb = new StringBuilder();
foreach (Node node in drinksSelfOrganizingMap.NeuralNet)
{
writer.WriteStartElement("Node");
writer.WriteElementString("DrinkID", node.DrinkID.ToString());
writer.WriteElementString("X", node.X.ToString());
writer.WriteElementString("Y", node.Y.ToString());
sb.Clear();
for (int i = 0; i < node.Weights.Length - 1; i++)
{
sb.Append(node.Weights[i]);
sb.Append(";");
}
sb.Append(node.Weights[node.Weights.Length - 1]);
writer.WriteElementString("Weights", sb.ToString());
writer.WriteEndElement();
}
writer.WriteEndElement();
writer.WriteEndElement();
writer.WriteEndDocument();
}
}
public Image GetRender(DrinksSelfOrganizingMap drinksMap)
{
Bitmap render = new Bitmap((int)(drinksMap.NeuralMapWidth * drinksMap.DistanceBetweenNeurons * SCALE_FACTOR), (int)(drinksMap.NeuralMapHeight * drinksMap.DistanceBetweenNeurons * SCALE_FACTOR));
using (Graphics g = Graphics.FromImage(render))
{
g.TextRenderingHint = System.Drawing.Text.TextRenderingHint.ClearTypeGridFit;
g.CompositingQuality = System.Drawing.Drawing2D.CompositingQuality.HighQuality;
g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
g.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.HighQuality;
g.FillRectangle(Brushes.WhiteSmoke, 0, 0, render.Width, render.Height);
foreach (Node node in drinksMap.NeuralNet)
{
g.DrawString(node.DrinkID.ToString(), SystemFonts.MessageBoxFont, GetDrinkBrushById(node.DrinkID),
(float)(node.X * drinksMap.DistanceBetweenNeurons * SCALE_FACTOR),
(float)(node.Y * drinksMap.DistanceBetweenNeurons * SCALE_FACTOR));
}
}
return render;
}
public void LoadNeuralNet(string filename)
{
NeuralNetReader reader = new NeuralNetReader();
drinksMap = reader.LoadNeuralNet(filename);
}
public void LearnNeuralNet(int epochsCount, double initialLearningRate, float distanceBetweenNeurons, int neuralMapWidth,
int neuralMapHeight, double minNeuronPotential, int maxNeuronRestTime)
{
NeuralNetTeacher learner = new NeuralNetTeacher(DrinksContainer.DrinksDictionary, epochsCount, initialLearningRate,
distanceBetweenNeurons, neuralMapWidth, neuralMapHeight, DrinksContainer.FeaturesCount, minNeuronPotential, maxNeuronRestTime);
drinksMap = learner.GetLearnedNeuralNet();
}
